With the growth of information technology (IT) industries, the production of information entities has recently accelerated rapidly. Large software vendors may need to maintain increasing amounts of software artifacts upon which their products are based. Reuse of existing entities has been a common approach to attempt to reduce further development and maintenance costs. This task typically requires extra work, and is thus expensive. However, the “time to market” of new or adapted products and services has been an important measure for a company to succeed in a quickly changing and highly competitive industry sector. Thus, there may exist a trade-off between software quality and the agility of an IT company which may impact its success. Therefore, effective techniques for supporting software quality assurance may free resources to assist in a company's competitive advantage.
An object-oriented approach to re-use has led to the development of local libraries of software components that may use proprietary objects or data types to define their inputs and outputs. An industrial need for a more flexible integration of companies has led to the development of Web service technologies that has provided standardized languages to specify the storage (e.g., Universal Description, Discovery and Integration (UDDI)), interfaces (e.g., Web Service Description Language (WSDL)) and communication (e.g., Simple Object Access Protocol (SOAP)) of software components that are currently available over the Internet. These standards anticipate the data that Web services exchange to be eXtensible Markup Language (XML) data types, defined by XML schema definitions (XSD).
In the area of data mining, a “closed frequent itemset mining” technique may be used for the mining of frequent patterns, associations, and correlations, originally intended as part of a market basket analysis. Gaining knowledge regarding which products are purchased simultaneously in one business transaction by a customer has become an important part of the design of many marketing strategies. Conventional association mining algorithms may try to extract information regarding which “itemsets” may appear as a whole in different “transactions.” For example, the mining algorithms may try to extract information such as names of items such as “bread” and “butter” that may have been purchased simultaneously by “Bob” in transaction T1 and by “Jane” in transaction T2. For example, such information may be useful in marketing decisions regarding whether to place bread and butter in close proximity to each other in a supermarket.
Various conventional frequent itemset mining approaches may be categorized as either horizontal or vertical techniques. Thus, example horizontal approaches may save for every parent data type a list of all of its sub data types. Example vertical approaches save for every sub data type the list of all parents where the sub data type appears. Example conventional vertical approaches may build candidate n-itemsets step by step, and then explore these itemsets in their data base. Thus, these conventional algorithms may be categorized as “one-shot” techniques. Thus, it may be desirable to provide techniques which may improve efficiencies by providing results at an early stage in processing.